计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3261-3269.DOI: 10.11772/j.issn.1001-9081.2017.11.3261
收稿日期:
2017-05-19
修回日期:
2017-07-27
出版日期:
2017-11-10
发布日期:
2017-11-11
通讯作者:
张雪英
作者简介:
乔建华(1975-),女,山西吕梁人,副教授,博士研究生,主要研究方向:无线传感器网络、压缩感知;张雪英(1964-),女,河北行唐人,教授,博士,主要研究方向:语音信号处理、多媒体通信、物联网。
基金资助:
QIAO Jianhua1,2, ZHANG Xueying1
Received:
2017-05-19
Revised:
2017-07-27
Online:
2017-11-10
Published:
2017-11-11
Supported by:
摘要: 为了对无线传感器网络的压缩数据收集有一个全面的认识和评估,对到目前为止国内外的相关研究成果作了一个系统的介绍。首先,介绍了压缩数据收集及改进方法的框架的建立;然后,分别根据无线传感器网络的传输模式和压缩感知理论的三要素,对压缩数据收集方法分类进行了阐述;接下来,说明了压缩数据收集的自适应和优化问题,与其他方法的联合应用,及实际应用范例;最后,指出了压缩数据收集存在的问题和未来的发展方向。
中图分类号:
乔建华, 张雪英. 基于压缩感知的无线传感器网络数据收集研究综述[J]. 计算机应用, 2017, 37(11): 3261-3269.
QIAO Jianhua, ZHANG Xueying. Compressed sensing based data gathering in wireless sensor networks: a survey[J]. Journal of Computer Applications, 2017, 37(11): 3261-3269.
[1] KHAN I, BELQASMI F, GLITHO R, et al. Wireless sensor network virtualization:a survey[J]. IEEE Communications Surveys and Tutorials, 2016, 18(1):553-576. [2] MISHRA S, THAKKAR H. Features of WSN and data aggregation techniques in WSN:a survey[J].International Journal of Engineering and Innovative Technology, 2012,1(4):264-273. [3] RAWAT P, SINGH K D, CHAOUCHI H, et al. Wireless sensor networks:a survey on recent developments and potential synergies[J]. Journal of Supercomputing, 2014, 68(1):1-48. [4] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. [5] BARANIUK R. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4):118-121. [6] CANDōS E J, WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2):21-30. [7] CANDōS E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9/10):589-592. [8] TROPP J, GILBERT A. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666. [9] BAJWA W, HAUPT J, SAYEED A, et al. Compressive wireless sensing[C]//Proceedings of the 5th International Conference on Information Processing in Sensor Networks. New York:ACM, 2006:134-142. [10] HAUPT J, BAJWA W U, RABBAT M, et al. Compressed sensing for networked data[J]. IEEE Signal Processing Magazine, 2008, 25(2):92-101. [11] LUO J, XIANG L, ROSENBERG C. Does compressed sensing improve the throughput of wireless sensor networks?[C]//Proceedings of the 2010 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2010.5:1-6. [12] LUO C, WU F, SUN J, et al. Compressive data gathering for large-scale wireless sensor networks[C]//Proceedings of the 15th Annual International Conference on Mobile Computing and Networking. New York:ACM, 2009.9:145-156. [13] LUO C,WU F, SUN J, et al. Efficient measurement generation and pervasive sparsity for compressive data gathering[J].IEEE Transactions on Wireless Communications, 2010, 9(12):3728-3738. [14] CHEN W, WASSELL I J. Energy-efficient signal acquisition in wireless sensor networks:a compressive sensing framework[J]. IET Wireless Sensor Systems, 2012, 2(1):1-8. [15] LI S C, XU L D, WANG X H. Compressed sensing signal and data acquisition in wireless sensor networks and internet of things[J]. IEEE Transactions on Industrial Informatics, 2013, 9(4):2177-2186. [16] YANG G, XIAO M, ZHANG S. Data aggregation scheme based on compressed sensing in wireless sensor network[J]. Journal of Networks, 2013, 8(1):556-561. [17] YANG G, TAN V Y F, HO C K, et al. Wireless compressive sensing for energy harvesting sensor nodes[J]. IEEE Transactions on Signal Processing, 2013, 61(18):4491-4505. [18] TANG Y, ZHANG B, JING T, et al. Robust compressive data gathering in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(6):2754-2761. [19] BARCELó-LLADóJ E, MORELL A, SECO-GRANADOS G. Amplify-and-forward compressed sensing as an energy-efficient solution in wireless sensor networks[J]. IEEE Sensors Journal, 2014, 14(5):1710-1719. [20] LIU X Y, ZHU Y M, KONG L H, et al. CDC:compressive data collection for wireless sensor networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(8):2188-2197. [21] YIN H, LI J, CHAI Y,et al. A survey on distributed compressed sensing:theory and applications[J]. Frontiers of Computer Science, 2014, 8(6):893-904. [22] BARON D, WAKIN M B, DUARTE M F, et al. Distributed compressed sensing[EB/OL].[2009-01-22]. http://webee.technion.ac.il/people/drorb/pdf/DCS012009.pdf. [23] WANG W, WANG D, JIANG Y. Energy efficient distributed compressed data gathering for sensor networks[J]. Ad Hoc Networks, 2017, 58:112-117. [24] MASOUM A, MERATNIA N, HAVINGA P J M. A distributed compressive sensing technique for data gathering in wireless sensor networks[J]. Procedia Computer Science, 2013, 21(4):207-216. [25] WANG Q, LIU Z. A robust and efficient algorithm for distributed compressed sensing[J]. Computers and Electrical Engineering, 2011, 37(6):916-926. [26] LIANG J, MAO C. Distributed compressive sensing in heterogeneous sensor network[J]. Signal Processing, 2016, 126(C):96-102. [27] TSAI T Y, LAN W C, LIU C, et al. Distributed compressive data aggregation in large-scale wireless sensor networks[J]. Journal of Advances of Computer Networks, 2013, 1(4):295-300. [28] CAIONE C, BRUNELLI D, BENINI L. Distributed compressive sampling for lifetime optimization in dense wireless sensor networks[J]. IEEE Transactions on Industrial Informatics, 2012, 8(1):30-40. [29] HAUPT J, NOWAK R. Signal reconstruction from noisy random projections[J]. IEEE Transactions on Information Theory, 2006, 52(9):4036-4048. [30] HAUPT J, NOWAK R. Signal reconstruction from noisy randomized projections with applications to wireless sensing[C]//Proceedings of the 2005 IEEE/SP 13th Workshop on Statistical Signal Processing. Piscataway, NJ:IEEE, 2005:1182-1187. [31] WANG W, GAROFALAKIS M, RAMCHANDRAN K. Distributed sparse random projections for refinable approximation[C]//Proceedings of the 20076th International Symposium on Information Processing in Sensor Networks,2007:331-339. [32] EBRAHIMI D, ASSI C. Compressive data gathering using random projection for energy efficient wireless sensor networks[J].Ad Hoc Networks, 2014, 16:105-119. [33] WIMALAJEEWA T, VARSHNEY P K. Wireless compressive sensing over fading channels with distributed sparse random projections[J]. IEEE Transactions on Signal and Information Processing over Networks, 2015, 1(1):33-44. [34] RAN R, OH H Y. Adaptive sparse random projections for wireless sensor networks with energy harvesting constraints[J]. EURASIP Journal on Wireless Communications and Networking, 2015(1):113. [35] NGUYEN M T, RAHNAVARD N. Cluster-based energy-efficient data collection in wireless sensor networks utilizing compressive sensing[C]//Proceedings of the 2013 Military Communications Conference. Piscataway, NJ:IEEE, 2013:1708-1713. [36] NGUYEN M T, TEAGUE K A, RAHNAVARD N. CCS:energy-efficient data collection in clustered wireless sensor networks utilizing block-wise compressive sensing[J]. Computer Networks, 2016, 106:171-185. [37] WU X G, XIONG Y, HUANG W C, et al. An efficient compressive data gathering routing scheme for large-scale wireless sensor networks[J].Computers and Electrical Engineering, 2013,39(6):1935-1946. [38] ZOU Z, HU C, ZHANG F,et al. WSNs data acquisition by combining hierarchical routing method and compressive sensing[J]. Sensors, 2014, 14(9):16766-16784. [39] ARUNRAJA M,MALATHI V, SAKTHIVEL E. Distributed similarity based clustering and compressed forwarding for wireless sensor networks[J]. ISA Transactions, 2015, 59:180. [40] XIE R T, JIA X H. Transmission-efficient clustering method for wireless sensor networks using compressive sensing[J]. IEEE Transactions on Parallel and Distributed Systems, 2014, 25(3):806-815. [41] LAN K C, WEI M Z. A compressibility-based clustering algorithm for hierarchical compressive data gathering[J]. IEEE Sensors Journal, 2017, 17(8):2550-2562. [42] ABBASI-DARESARI S, ABOUEI J. Toward cluster-based weighted compressive data aggregation in wireless sensor networks[J]. Ad Hoc Networks, 2016, 36(1):368-385. [43] XU X, ANSARI R, KHOKHAR A, et al. Hierarchical Data Aggregation using Compressive Sensing (HDACS) in WSNs[J]. Acm Transactions on Sensor Networks, 2015, 11(3):1-25. [44] EBRAHIMI D, ASSI C. Optimal and efficient algorithms for projection-based compressive data gathering[J]. IEEE Communications Letters, 2013, 17(8):1572-1575. [45] CHEN Z, YANG G, CHEN L, et al. Constructing maximum-lifetime data-gathering tree in WSNs based on compressed sensing[J]. International Journal of Distributed Sensor Networks, 2016(1):1-11. [46] XIAO F, GE G W, SUN L J, et al. An energy-efficient data gathering method based on compressive sensing for pervasive sensor networks[EB/OL].[2017-02-16].https://doi.org/10.1016/j.pmcj.2017.02.005. [47] FLETCHER R B. Energy efficient compressed sensing in wireless sensor networks via random walk[EB/OL].[2016-11-20]. https://search.proquest.com/docview/871836069. [48] SARTIPI M, FLETCHER R. Energy-efficient data acquisition in wireless sensor networks using compressed sensing[C]//Proceedings of the 2011 Data Compression Conference. Washington, DC:IEEE Computer Society, 2011:223-232. [49] NGUYEN M T. Minimizing energy consumption in random walk routing for wireless sensor networks utilizing compressed sensing[C]//Proceedings of the 2013 International Conference on System of Systems Engineering. Piscataway, NJ:IEEE, 2013:297-301. [50] NGUYEN M T, TEAGUE K A. Compressive sensing based random walk routing in wireless sensor networks[J]. Ad Hoc Networks, 2017, 54:99-110. [51] JIANG L, LIU A, HU Y, CHEN Z. Lifetime maximization through dynamic ring-based routing scheme for correlated data collecting in WSNs[J].Computers & Electrical Engineering, 2015, 41(1):191-215. [52] YAO Y, CAO Q, VASILAKOS A V. EDAL:an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks[J]. IEEE/ACM Transactions on Networking, 2015, 23(3):810-823. [53] MEHRJOO S, SHANBEHZADEH J, PEDRAM M M. A novel intelligent energy-efficient delay-aware routing in WSN, based on compressive sensing[C]//Proceedings of the 20105th International Symposium on Telecommunications. Piscataway, NJ:IEEE, 2010:415-420. [54] KUILA P, JANA P K. Energy efficient clustering and routing algorithms for wireless sensor networks:Particle swarm optimization approach[J]. Engineering Applications of Artificial Intelligence, 2014, 33(1):127-140. [55] 王强,张培林,王怀光,等. 压缩感知中测量矩阵构造综述[J]. 计算机应用,2017,37(1):188-196.(WANG Q, ZHANG P L, WANG H G, et al. A survey on the construction of measurement matrices in compressive sensing[J]. Journal of Computer Applications, 2017, 37(1):188-196.) [56] LI N, SAWAN M. Neural signal compression using a minimum Euclidean or Manhattan distance cluster-based deterministic compressed sensing matrix[J]. Biomedical Signal Processing and Control, 2015, 19:44-55. [57] LV C C,WANG Q,YAN W J, SHEN Y. Energy-balanced compressive data gathering in wireless sensor networks[J].Journal of Network and Computer Applications, 2016, 61(C):102-114. [58] QIAO J H, ZHANG X Y. The design of a dual-structured measurement matrix in compressed sensing[C]//Proceedings of the 2016 IEEE International Conference on Information and Automation. Piscataway, NJ:IEEE, 2016:184-188. [59] CHENG J, YE Q, JIANG H B, et al. STCDG:an efficient data gathering algorithm based on matrix completion for wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(2):850-861. [60] EL-TELBANY M E, MAGED M A. Exploiting sparsity in wireless sensor networks for energy saving:a comparative study[J]. International Journal of Applied Engineering Research, 2017, 12(4):452-460. [61] HE J F, SUN G L, LI Z Z, et al. Compressive data gathering with low-rank constraints for wireless sensor networks[J]. Signal Processing, 2017, 131:73-76. [62] QUER G, MASIERO R, MUNARETTO D, et al. On the interplay between routing and signal representation for compressive sensing in wireless sensor networks[J]. Information Theory and Applications Workshop, 2009, 10(1):206-215. [63] LEE S, ORTEGA A. Efficient data-gathering using graph-based transform and compressed sensing for irregularly positioned sensors[C]//Proceedings of the 2013 Asia-Pacific Signal and Information Processing Association Summit and Conference. Piscataway, NJ:IEEE, 2013:1-4. [64] XIANG L, LUO J, ROSENBERG C. Compressed data aggregation:energy-efficient and high-fidelity data collection[J]. IEEE/ACM Transactions on Networking, 2013, 21(6):1722-1735. [65] NGUYEN M, TEAGUE K. Energy-efficient data collection in clustered wireless sensor networks employing distributed DCT[J]. International Journal of Wireless and Mobile Networks, 2016, 8(5):1-18. [66] WANG T, LU X, YU X, et al. A fast and accurate sparse continuous signal reconstruction by homotopy DCD with non-convex regularization[J].Sensors, 2014, 14(4):5929-5951. [67] BRUNELLI D, CAIONE C. Sparse recovery optimization in wireless sensor networks with a sub-Nyquist sampling rate[J]. Sensors, 2014, 15(7):16654-16673. [68] CHOU C T, RAJJB RANA, HU W. Energy efficient information collection in wireless sensor networks using adaptive compressive sensing[C]//Proceedings of the 34th IEEE Conference on Local Computer Networks. Piscataway, NJ:IEEE, 2009:20-23. [69] LIU Z, ZHANG M, CUI J. An adaptive data collection algorithm based on a Bayesian compressed sensing framework[J]. Sensors, 2014, 14(5):8330-8349. [70] CHEN Z, RANIERI J, ZHANG R, et al. DASS:Distributed adaptive sparse sensing[J]. IEEE Transactions on Wireless Communications, 2015, 14(5):2571-2583. [71] HAO J, ZHANG B X, JIAO Z Z, et al. Adaptive compressive sensing based sample scheduling mechanism for wireless sensor networks[J]. Pervasive and Mobile Computing, 2016, 22(9):113-125. [72] RADOVIC M, DUKNIC M, TASESKI J. Sensing, compression, and recovery for WSNs:sparse signal modeling and monitoring framework[J]. IEEE Transactions on Wireless Communications, 2012, 11(10):3447-3461. [73] CHEN S G, ZHAO C X, WU M, et al. Compressive network coding for wireless sensor networks:spatio-temporal coding and optimization design[J]. Computer Networks, 2016, 108:345-356. [74] YIN J, YANG Y W, WANG L. A reliable data transmission scheme based on compressed sensing and network coding for multi-hop-relay wireless sensor networks[J]. Computers and Electrical Engineering, 2016, 56(C):366-384. |
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